Triple
T1489730
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gauss’s lemma (number theory) |
E29548
|
entity |
| Predicate | toolIn |
P28572
|
FINISHED |
| Object | computational number theory |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: computational number theory | Statement: [Gauss’s lemma (number theory), toolIn, computational number theory]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: toolIn Context triple: [Gauss’s lemma (number theory), toolIn, computational number theory]
-
A.
toolUsed
Indicates that an action or task is performed using a particular tool as the means or instrument.
-
B.
isToolOf
Indicates that one entity functions as an instrument or means used by another entity to perform tasks or achieve goals.
-
C.
usedByTool
Indicates that a particular tool is employed or operated by a specified agent or entity.
-
D.
toolUseExamples
Indicates that one entity provides example instances or demonstrations of how a particular tool is or can be used by another entity.
-
E.
attackToolExample
Indicates that a specific tool or method is used as an example of how an attack is or can be carried out.
- F. None of above. chosen
Provenance (4 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69a498da82e08190ba833330d05f380f |
completed | March 1, 2026, 7:51 p.m. |
| NER | Named-entity recognition | batch_69a4c6c233ec819087e1233af02aabfc |
completed | March 1, 2026, 11:07 p.m. |
| PD | Predicate disambiguation | batch_69a4c48902808190a8028d359bcf123e |
completed | March 1, 2026, 10:58 p.m. |
| PDg | Predicate description generation | batch_69a4c52c703c8190a56389b09d97659f |
completed | March 1, 2026, 11:01 p.m. |
Created at: March 1, 2026, 8:12 p.m.